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Boris Kontsevoi is a technology executive, President and CEO of <\/em>Intetics Inc.<\/em><\/a>, a global software engineering and data processing company.<\/em><\/p>\n Earlier this year, a product team at a midsized SaaS company shared an unexpected issue. They had integrated AI into their customer support workflow to speed up responses. It worked well at first: Tickets were resolved faster, and customer satisfaction improved. But after a few weeks, edge cases started to appear: responses that were technically correct but contextually wrong, subtle inconsistencies in tone and decisions that no one on the team could fully trace back to a clear rule or logic.<\/p>\n Nothing was \u201cbroken\u201d in the traditional sense. The system was working. But it was working in a way that the team could no longer fully explain or control.<\/p>\n That moment captures why the conversation around AI is shifting so quickly from excitement to control.<\/p>\n \u200bTraditional software behaves deterministically. Given the same input, it produces the same output. But AI systems don\u2019t. They adapt, evolve and sometimes behave differently at the edges of their training data. They can degrade over time as the world changes. They can amplify patterns that were never explicitly designed. And most importantly, they often operate without a clear line of accountability for individual decisions. This gap is a structural one.\u200b<\/p>\n<\/section>\n This is where the EU AI Act<\/a> enters the picture. It is one of the first large-scale attempts to treat AI as a living system that requires continuous oversight. The logic is surprisingly simple: Not all AI carries the same risk. For example, a recommendation engine suggesting movies is very different from an AI system supporting medical diagnosis or credit decisions. The EU AI Act reflects this by introducing a risk-based model:<\/p>\n \u2022 Unacceptable Risk:<\/strong> Systems that are prohibited entirely<\/p>\n \u2022 High Risk: <\/strong>Systems that require strict controls, documentation and monitoring<\/p>\n \u2022 Limited And Minimal Risk:<\/strong> Systems with lighter obligations<\/p>\n What matters here is not the classification itself, but the shift in mindset that made people choose this approach. The Act assumes that AI must be managed over time, not just approved once. This moves responsibility from \u201cwhat the system is\u201d to \u201chow the system behaves in real life.\u201d\u200b<\/p>\n<\/section>\n For many companies, regulation still feels like a future problem. In practice, it is already an operational one. AI is embedded in delivery pipelines, customer interactions, analytics and decision support systems. Even when organizations do not label themselves as \u201cAI companies,\u201d they are already running AI-powered processes.<\/p>\n This creates a simple tension: The speed of adoption is high, while the level of control is often low. And this is exactly where costs begin to grow\u2014not immediately, but later.<\/p>\n When audits, regulators or enterprise clients start asking questions, companies realize they cannot easily show:<\/p>\n \u2022 How their models were trained<\/p>\n \u2022 How decisions are made<\/p>\n \u2022 How risks are monitored<\/p>\n \u2022 Who is accountable<\/p>\n At that point, governance becomes reactive. And expensive.\u200b<\/p>\n<\/section>\n This is why regulation alone is not enough. Laws define what must be achieved. Businesses still need a way to operate within those expectations every day. And logically, a thing like ISO\/IEC 42001 becomes important. Unlike regulatory frameworks, ISO standards are not about restriction; they are about discipline.<\/p>\n ISO\/IEC 42001<\/a> introduces the idea of an AI management system, a structured way to ensure that AI is developed, deployed and monitored responsibly. It focuses on practical questions:<\/p>\n \u2022 Is there clear ownership of AI systems?<\/p>\n \u2022 Are risks identified and continuously monitored?<\/p>\n \u2022 Is there traceability from input data to output decisions?<\/p>\n \u2022 Are processes repeatable and auditable?<\/p>\n In other words, it turns abstract governance into daily operational routines.\u200b<\/p>\n<\/section>\n If we step back, something larger becomes visible. GDPR established control over data, the EU AI Act establishes control over behavior and ISO\/IEC 42001 establishes control over process. Together, they form a layered system:<\/p>\n \u2022 Data is protected.<\/p>\n \u2022 AI systems are classified and monitored.<\/p>\n \u2022 Organizations build internal discipline to manage both.<\/p>\n This is not accidental. It reflects a broader shift where AI is no longer treated as an experiment; it is becoming infrastructure. And infrastructure always requires standards.\u200b<\/p>\n<\/section>\n At first glance, all of this may look like friction: more controls, documentation and responsibility. But in practice, the opposite is happening: Companies that introduce structure early tend to move faster later. They can avoid rework, reduce hidden risks and build systems that can scale without breaking under pressure. AI amplifies whatever environment it operates in. In a chaotic system, it accelerates chaos. In a structured system, it becomes a force multiplier of clarity and performance.<\/p>\n<\/section>\n Try to think of AI as an engine. The EU AI Act defines the rules of the road, ISO\/IEC 42001 defines how the vehicle is built and maintained, and frameworks like GDPR ensure that passengers are protected. None of these elements work well in isolation. But together, they create something that did not exist before: a system where innovation and control are designed to coexist.\u200b<\/p>\n<\/section>\n We are entering a phase where technical capability is no longer the bottleneck for AI adoption; organizational readiness is. The companies that understand this early are already shifting their focus from tools to structure, from speed to sustainability and from experimentation to controlled execution.<\/p>\n One practical implication is becoming increasingly clear: Building compliant, production-ready AI systems from scratch is both complex and time-consuming. As a result, many organizations are accelerating their path to compliance by working with certified engineering partners who already operate within frameworks like ISO\/IEC 42001 and align with regulatory requirements such as the EU AI Act.<\/p>\n This approach allows companies to move faster without compromising control, leveraging established processes, audit-ready systems and teams that understand how to balance innovation with accountability.<\/p>\n In that sense, compliance is no longer just a legal necessity. It is becoming a strategic shortcut to faster time-to-market and, ultimately, a way to outperform competitors who are still trying to build that discipline internally.<\/p>\n Forbes Technology Council<\/u><\/a> is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?<\/u><\/em><\/a><\/p>\nWhy Existing Frameworks Were Not Enough<\/h2>\n
What The EU AI Act Changes<\/h2>\n
Why This Matters For Businesses Now<\/h2>\n
From Regulation To Operating Discipline<\/h2>\n
Why ISO 42001 Fits The Bigger Picture<\/h2>\n
The Real Outcome: Slowing Down To Move Faster<\/h2>\n
A Practical Way To Think About It<\/h2>\n
What Comes Next<\/h2>\n
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